Data Mining: Concepts And Techniques

Data Mining: Concepts and Techniques (repost)  

Posted by tot167 at March 11, 2011
Data Mining: Concepts and Techniques (repost)

Jiawei Han, Micheline Kamber, "Data Mining: Concepts and Techniques"
M..gan K..fmann | 2000 | ISBN: 1558604898 | 550 pages | Djvu | 5,4 MB
Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection (repost)

Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection by Peter Christen
English | 2012 | ISBN-10: 3642311636 | PDF | 289 pages | 2,8 MB

Data matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database.
Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection (repost)

Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection by Peter Christen
English | 2012 | ISBN-10: 3642311636 | PDF | 289 pages | 2,8 MB

Data matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database.
Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques

Benjamin C.M. Fung, Ke Wang, Ada Wai-Chee Fu, Philip S. Yu, "Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques"
English | 2010 | ISBN: 1420091484 | PDF | 376 pages | 5.08 MB
Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection

Peter Christen, "Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection"
2012 | ISBN-10: 3642311636 | PDF | 289 pages | 4 MB
Data Mining: Concepts, Models, Methods, and Algorithms [Repost]

Data Mining: Concepts, Models, Methods, and Algorithms by Mehmed Kantardzic
Wiley-IEEE Press | October 25 2002 | ISBN: 0471228524 | Pages: 360 | CHM | 8.44 MB

A comprehensive introduction to the exploding field of data mining. We are surrounded by data, numerical and otherwise, which must be analyzed and processed to convert it into information that informs, instructs, answers, or otherwise aids understanding and decision-making. Due to the ever-increasing complexity and size of today's data sets, a new term, data mining, was created to describe the indirect, automatic data analysis techniques that utilize more complex and sophisticated tools than those which analysts used in the past to do mere data analysis.
Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (repost)

Alexander Paprotny, Michael Thess, "Realtime Data Mining: Self-Learning Techniques for Recommendation Engines: Toward the Self-Learning Recommendation Engine"
English | ISBN: 3319013203 | 2013 | 297 pages | PDF | 4 MB

Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Engines features a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and hierarchical methods. Furthermore, it presents promising results of numerous experiments on real-world data.​ The area of realtime data mining is currently developing at an exceptionally dynamic pace, and realtime data mining systems are the counterpart of today's “classic” data mining systems. Whereas the latter learn from historical data and then use it to deduce necessary actions, realtime analytics systems learn and act continuously and autonomously. In the vanguard of these new analytics systems are recommendation engines. They are principally found on the Internet, where all information is available in realtime and an immediate feedback is guaranteed.
Visual Data Mining w/WS: Techniques and Tools for Data Visualization and Mining (repost)

Visual Data Mining w/WS: Techniques and Tools for Data Visualization and Mining by Tom Soukup
English | 16 May 2002 | ISBN: 0471149993 | 424 Pages | PDF | 20 MB

Marketing analysts use data mining techniques to gain a reliable understanding of customer buying habits and then use that information to develop new marketing campaigns and products. Visual mining tools introduce a world of possibilities to a much broader and non–technical audience to help them solve common business problems.

Pattern Discovery Using Sequence Data Mining: Applications and Studies (Repost)  eBooks & eLearning

Posted by roxul at July 19, 2016
Pattern Discovery Using Sequence Data Mining: Applications and Studies (Repost)

Pradeep Kumar, Pradeep Kumar, P. Radha Krishna and S. Bapi Raju, "Pattern Discovery Using Sequence Data Mining: Applications and Studies"
English | ISBN: 1613500564 | 2011 | 286 pages | PDF | 5 MB

Spatial Network Data: Concepts and Techniques for Summarization  eBooks & eLearning

Posted by Underaglassmoon at June 28, 2016
Spatial Network Data: Concepts and Techniques for Summarization

Spatial Network Data: Concepts and Techniques for Summarization
Springer | Communication Networks | July 19, 2016 | ISBN-10: 331939620X | 46 pages | pdf | 2.86 mb

Authors: Oliver, Dev